Abstract
Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool is presented. The Density Visualization Pipeline (DVP) computes, visualizes and quantifies the density distribution, i.e., the “mass” of interneurons. We use the DVP to characterize and classify a set of GABAergic interneurons. Classification of GABAergic interneurons is of crucial importance to understand on the one hand their various functions and on the other hand their ubiquitous appearance in the neocortex. 3D density map visualization and projection to the one-dimensional x, y, z subspaces show a clear distinction between the studied cells, based on these metrics. The DVP can be coupled to computational studies of the behavior of neurons and networks, in which network topology information is derived from DVP information. The DVP reads common neuromorphological file formats, e.g., Neurolucida XML files, NeuroMorpho.org SWC files and plain ASCII files. Full 3D visualization and projections of the density to 1D and 2D manifolds are supported by the DVP. All routines are embedded within the visual programming IDE VRL-Studio for Java which allows the definition and rapid modification of analysis workflows.
Highlights
The stunning diversity of neuronal morphologies is being studied since the work of Cajal (Ramón y Cajal, 1899) a century ago
1D and 2D density maps have been employed for such neuroanatomical analysis, 3D density maps can potentially reveal characteristics of the cell morphologies which might be lost during projection
This paper presents the Density Visualization Pipeline (DVP), a project that focusses on three-dimensional visualization and analysis of cellular mass distribution for cell classification
Summary
The stunning diversity of neuronal morphologies is being studied since the work of Cajal (Ramón y Cajal, 1899) a century ago. The somato-dendritic and axonal morphologies reflect the neuronal input and output patterns: The former indicates the number and location of synaptic inputs while the latter defines the spatial distribution of synaptic outputs. With the development of neuronal reconstruction techniques [e.g., the Neurolucida system (MicroBrightField) (Aguiar et al, 2013) and (Halavi et al, 2012)] and several large-scale brain research projects and novel reconstruction methods (Peng et al, 2010, 2014; Bria et al, 2016), digital neuronal morphologies have been systematically acquired and became freely accessible, via projects and databases, such as NeuroMorpho.org (Ascoli et al, 2007), The Blue Brain Project (Markram et al, 2015), Allen Cell Types Database, and Allen Brain Atlas (Sunkin et al, 2012; Gouwens et al, 2019)
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